Decorrelating ReSTIR Samplers via MCMC Mutations

Sawhney, Rohan; Lin, Daqi; Kettunen, Markus; Bitterli, Benedikt; Ramamoorthi, Ravi; Wyman, Chris; Pharr, Matt

doi:10.48550/arxiv.2211.00166

2022

DOI: 10.48550/arxiv.2211.00166

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Decorrelating ReSTIR Samplers via MCMC Mutations

Rohan Sawhney¹,

Daqi Lin²,

Markus Kettunen³

et al.

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Efficient Spatial Resampling Using the PDF Similarity

Tokuyoshi¹

2023

Proc. ACM Comput. Graph. Interact. Tech.

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In real-time rendering, spatiotemporal reservoir resampling (ReSTIR) is a powerful technique to increase the number of candidate samples for resampled importance sampling. However, reusing spatiotemporal samples is not always efficient when target PDFs for the reused samples are dissimilar to the integrand. Target PDFs are often spatially different for highly detailed scenes due to geometry edges, normal maps, spatially varying materials, and shadow edges. This paper introduces a new method of rejecting spatial reuse based on the similarity of PDF shapes for single-bounce path connections (e.g., direct illumination). While existing rejection methods for ReSTIR do not support arbitrary materials and shadow edges, our PDF similarity takes them into account because target PDFs include BSDFs and shadows. In this paper, we present a rough estimation of PDF shapes using von Mises--Fisher distributions and temporal resampling. We also present a stable combination of our rejection method and the existing rejection method, considering estimation errors due to temporal disocclusions and moving light sources. This combination efficiently reduces the error around shadow edges with temporal continuities. By using our method for a ReSTIR variant that reuses shadow ray visibility for the integrand, we can reduce the number of shadow rays while preserving shadow edges.

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Efficient Spatial Resampling Using the PDF Similarity

Tokuyoshi¹

2023

Proc. ACM Comput. Graph. Interact. Tech.

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Decorrelating ReSTIR Samplers via MCMC Mutations

Cited by 1 publication

References 24 publications

Efficient Spatial Resampling Using the PDF Similarity

Efficient Spatial Resampling Using the PDF Similarity

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